System and method for pivot-sample-based generator training

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for few-shot learning-based generator training based on raw data collected from a specific domain or class. In cases where the raw data is collected from multiple domains but is not easily divisible into classes, the invention describes training multiple generators based on a pivot-sample-based training process. Pivot samples are randomly selected from the raw data for clustering, and each cluster of raw data may be used to train a generator using the few-shot learning-based training process.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/506,317, filed on Oct. 20, 2021. The entire content of theabove-identified application is incorporated herein by reference.

TECHNICAL FIELD

The disclosure generally relates to training of artificial intelligence(AI), more particularly to pivot-sample-based generator training usingfew-shot learning for knowledge distillation.

BACKGROUND

In machine learning, knowledge distillation refers to transferringknowledge from a large model to a smaller one. The large model isusually called a teacher model, while the smaller model is usuallycalled a student model. Knowledge distillation transfers knowledgelearned by the teacher model from a large volume of training samplesinto the student model without loss of validity. Meanwhile, the studentmodel has a much smaller footprint and is less expensive to evaluate anddeploy.

Knowledge distillation involves training the student model to generatesimilar output as the teacher model does. This training process requirestraining samples. Theoretically, the training samples can be obtained byaccessing the original or augmented training samples that trained theteacher model. However, this is usually problematic in practice due toprivacy, proprietary, and availability concerns. To tackle thispractical problem, this disclosure describes a few-shot learning basedmethod for training a generator to generate training samples forknowledge distillation.

SUMMARY

Various embodiments of the present specification may include systems,methods, and non-transitory computer-readable media for training samplegenerator with few-shot learning.

According to one aspect, the method for training sample generator withfew-shot learning may include: obtaining a teacher model and a pluralityof training samples; generating a plurality of samples using agenerator; feeding the plurality of generated samples into the teachermodel to obtain a plurality of first statistics; feeding the pluralityof training samples into the teacher model to obtain a plurality secondstatistics; and training the generator to minimize a distance betweenthe plurality of first statistics and the plurality of secondstatistics.

In some embodiments, the method may further include: performingknowledge distillation from the teacher model to a student model usingthe trained generator.

In some embodiments, the feeding the plurality of generated samples intothe teacher model to obtain the plurality of first statistics comprises:feeding the plurality of generated samples into the teacher model; andobtaining the plurality of first statistics based on outputs of aplurality of layers in the teacher model when the plurality of generatedsamples are passing through the teacher model.

In some embodiments, the outputs comprise one or more tensors generatedby each layer of the teacher model, and the determining the plurality offirst statistics based on the one or more tensors from each layer of theteacher model comprises; for each of the one or more tensors,determining one or more channel-level statistics; and aggregating theone or more channel-level statistics from all layers of the teachermodel to obtain the plurality of first statistics.

In some embodiments, the one or more channel-level statistics compriseone or more of: a channel mean, a channel variance, and a channel k-thorder moment where k is greater than two.

In some embodiments, the outputs comprise one or more tensors generatedby each layer of the teacher model, and the plurality of firststatistics comprise a joint-covariance of all channels in each of theone or more tensors.

In some embodiments, the feeding the plurality of training samples intothe teacher model to obtain the plurality second statistics comprises:feeding the plurality of training samples into the teacher model;obtaining the plurality of second statistics based on outputs of aplurality of layers in the teacher model when the plurality of trainingsamples are passing through the teacher model.

In some embodiments, the method may further include constructing thestudent model with a smaller number of parameters than the teachermodel.

In some embodiments, the performing knowledge distillation from theteacher model to the student model using the trained generatorcomprises: generating a plurality of new training samples by using thetrained generator; feeding the plurality of new training samples intothe teacher model and the student model to obtain respective layer-leveloutputs of the teacher model and the student model; determining adistance between the layer-level outputs of the teacher model and thestudent model; and training the student model to minimize the distance.

In some embodiments, the layer-level outputs comprise feature vectorsgenerated by embedding layers of the teacher model and embedding layersof the student model.

In some embodiments, the teacher model is a pre-trained neural networkfor image classification, and the plurality of training samples arelabeled images.

In some embodiments, the teacher model is a pre-trained transformer fornatural language processing.

In some embodiments, the student model is trained to performclassification based on one or more features of an input, and a datadistribution of the plurality of new training samples with regarding tothe one or more features is within a threshold distance from a datadistribution of the plurality of training samples with regarding to theone or more features.

According to another aspect, a method for training a generator withfew-shot learning and pivot samples may include: obtaining a pluralityof training samples; randomly selecting a set of pivot samples from theplurality of training samples; based on the set of pivot samples,classifying the plurality of training samples to generate a set ofgroups of training samples respectively corresponding to the set ofpivot samples; and training a generator for each of the set of groups oftraining samples for generating new samples, wherein a data distributionof the new samples is within a threshold distance from a datadistribution of the group of training samples.

In some embodiments, the classifying the plurality of training samplesto generate a set of groups of training samples respectivelycorresponding to the set of pivot samples comprises: generating pivotvector representations for the set of pivot samples; for each of theplurality of training samples, generating a vector representation;determining distances between the vector representation and each of thepivot vector representations; identifying one of the set of pivotsamples corresponding to a pivot vector representation having a shortestdistance to the vector representation; and grouping the training samplewith the one pivot sample.

In some embodiments, the method may further include: receiving apre-trained teacher model, wherein the training a generator for each ofthe set of groups of training samples for generating new samplescomprises: initializing the generator; generating a plurality of samplesusing the generator; feeding the plurality of generated samples into theteacher model to obtain a plurality of first statistics; feeding theplurality of training samples into the teacher model to obtain aplurality second statistics; and training the generator to minimize adistance between the plurality of first statistics and the plurality ofsecond statistics.

In some embodiments, the feeding the plurality of generated samples intothe teacher model to obtain the plurality of first statistics comprises:feeding the plurality of generated samples into the teacher model; andobtaining the plurality of first statistics based on outputs of aplurality of layers in the teacher model when the plurality of generatedsamples are passing through the teacher model.

In some embodiments, the outputs comprise one or more tensors generatedby each layer of the teacher model, and the determining the plurality offirst statistics based on the one or more tensors from each layer of theteacher model comprises; for each of the one or more tensors,determining one or more channel-level statistics; and aggregating theone or more channel level statistics from all layers of the teachermodel to obtain the plurality of first statistics.

In some embodiments, the outputs comprise one or more tensors generatedby each layer of the teacher model, and the determining the plurality offirst statistics based on the one or more tensors from each layer of theteacher model comprises; for each of the one or more tensors,determining one or more channel-level statistics; and aggregating theone or more channel level statistics from all layers of the teachermodel to obtain the plurality of first statistics.

In some embodiments, the one or more channel-level statistics compriseone or more of: a channel mean, a channel variance, and a channel k-thorder moment where k is greater than two.

In some embodiments, the outputs comprise one or more tensors generatedby each layer of the teacher model, and the plurality of statisticscomprise a joint-covariance of all channels in each of the one or moretensors.

In some embodiments, the method may further include constructing astudent model with a smaller number of parameters than the teachermodel; and performing knowledge distillation from the teacher model to astudent model using the trained generators corresponding to the groupsof training samples.

In some embodiments, the performing knowledge distillation from theteacher model to the student model using the trained generatorscomprises: generating a plurality of new training samples by using eachof the trained generators; feeding the plurality of new training samplesinto the teacher model and the student model to obtain respectivelayer-level outputs of the teacher model and the student model;determining a distance between the layer-level outputs of the teachermodel and the student model; and training the student model to minimizethe distance.

In some embodiments, the layer-level outputs comprise feature vectorsgenerated by embedding layers of the teacher model and embedding layersof the student model.

In some embodiments, the teacher model is a pre-trained neural networkfor image classification, and the plurality of training samples arelabeled images.

In some embodiments, the teacher model is a pre-trained transformer fornatural language processing.

According to yet another aspect, a system may comprise one or moreprocessors and one or more non-transitory computer-readable memoriescoupled to the one or more processors and configured with instructionsexecutable by the one or more processors to cause the system to performany of the methods described herein.

According to still another aspect, a non-transitory computer-readablestorage medium may be configured with instructions executable by one ormore processors to cause the one or more processors to perform any ofthe methods described herein.

These and other features of the systems, methods, and non-transitorycomputer-readable media disclosed herein, as well as the methods ofoperation and functions of the related elements of structure and thecombination of parts and economies of manufacture, will become moreapparent upon consideration of the following description and theappended claims with reference to the accompanying drawings, all ofwhich form a part of this specification, wherein like reference numeralsdesignate corresponding parts in the various figures. It is to beexpressly understood, however, that the drawings are for purposes ofillustration and description only and are not intended as a definitionof the limits of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary knowledge distillation process inaccordance with various embodiments.

FIG. 2 illustrates an exemplary diagram for training a generator withfew-shot learning in accordance with various embodiments.

FIG. 3 illustrates an exemplary learning criteria for training agenerator with few-shot learning in accordance with various embodiments.

FIG. 4 illustrates an exemplary flow for training a generator withfew-shot learning in accordance with various embodiments.

FIG. 5 illustrates an exemplary method for training a generator withfew-shot learning in accordance with various embodiments.

FIG. 6A illustrates an exemplary method for class-based generatortraining in accordance with various embodiments.

FIG. 6B illustrates an exemplary method for pivot-sample-based generatortraining in accordance with various embodiments.

FIG. 6C illustrates an exemplary method training generators withfew-shot learning and pivot samples in accordance with variousembodiments.

FIG. 7 illustrates an example method for training generators with pivotsamples in accordance with various embodiments.

FIG. 8 illustrates an example computer system in which any of theembodiments described herein may be implemented.

DETAILED DESCRIPTION

Embodiments described herein provide methods, systems, apparatus fortraining sample generators for knowledge distillation between a teachermodel (a large machine learning model) and a student model (a smallmachine learning model). Knowledge distillation is one of the mostpopular and effective techniques for knowledge transfer and modelcompression. For example, a teacher model may be trained based on alarge volume of original training samples and made available for usersto download. After a user downloads the teacher model, it may not beable to deploy the bulky teacher model to less powerful devices such asmobile files or other edge devices. In this case, the user may train asmaller student model by transferring the knowledge from the teachermodel for easy deployment and less maintenance cost. However, it iscommon that the user does not have access to sufficient training samples(e.g., the teacher model training entity may not share its trainingsamples for data privacy, proprietary, or other reasons). In many cases,what the user has may be limited to a small set of self-developedtraining samples or a few training samples collected by him/herself whentesting the teacher model.

This disclosure describes a novel approach to perform knowledgedistillation when the available training samples are limited. Thisapproach works because of the following findings: when the goal is togenerate a large number of samples for model distillation, pruning orcompression, it is unnecessary to generate high-reality samples, whilethe ability to generate samples of task-specific features is moreimportant. For example, the teacher model may be trained based on amassive amount of training samples collected from numerous fields.Therefore, the teacher model may perform equally well in drasticallydifferent areas, such as classifying birds, classifying cars,classifying buildings, etc. However, the student model is usually moretask-specific and may only focus on a specific use case, such as genderclassification. In other words, the teacher model may look at thousandsof features of an input and thus require millions of parameters, whereasthe student model may only need to focus on a few dozens of features andthus have a much less number of parameters. To perform knowledgedistillation to train the student model for gender classification, thegenerated samples can focus on the most relevant features and ignoreother features. That is, the generated samples and the real samplesshould have similar data distribution with regarding only to therelevant features.

Based on the above-identified findings, a new training criteria (lossfunction) is designed to enable a few-shot learning-based generatortraining method. Here, “few-shot learning” refers to a type of machinelearning mechanism where the training sample set is limited.

Some embodiments in this disclosure further address the issue when thelimited training samples are collected from different domains, which maycause the new training criteria (loss function) to be inaccurate. Thecorresponding solution may be referred to as pivot-sample-basedgenerator training.

In the following description, specific, non-limiting embodiments of thepresent invention will be described with reference to the drawings.Particular features and aspects of any embodiment disclosed herein maybe used and/or combined with particular features and aspects of anyother embodiment disclosed herein. It should also be understood thatsuch embodiments are by way of example and are merely illustrative of asmall number of embodiments within the scope of the present invention.Various changes and modifications obvious to one skilled in the art towhich the present invention pertains are deemed to be within the spirit,scope, and contemplation of the present invention as further defined inthe appended claims.

FIG. 1 illustrates an exemplary knowledge distillation process inaccordance with various embodiments. The embodiments described in thisdisclosure may be implemented as a part of the illustrative knowledgedistillation process in FIG. 1 or another suitable knowledgedistillation or model compression process with limited training samples.

As shown, a teacher model 110 refers to a pre-trained machine learningmodel (e.g., a deep neural network or an ensemble of multiple smallmodels) based on a large volume of training samples 130. The teachermodel 110 may be trained by an entity with access to the large volume oftraining samples 130. The teacher model 110 may be intended to bepowerful and be able to perform well in many different machine learningtasks, such as performing accurate classifications in different areas.The teacher model 110 may have a huge amount of parameters to learnlatent relationships among the features of input data. As powerful theteacher model 110 is, it may be cumbersome for evaluation, deployment,and maintenance.

When a user 100 wants to use the teacher model 110, he or she may needto perform knowledge distillation to transfer the “knowledge” (theparameters for feature extraction, pattern recognition, etc.) of theteacher model 110 to a smaller student model 120. Generally, theknowledge distillation between the teacher model 110 and the studentmodel 120 involves feeding same training samples to both models, andtraining the student model 120 to behave as close as the teacher model110. Therefore, this process requires training samples. In practical,the user 100 may not have access to the large volume of training samples130 due to lack of authority, data privacy, proprietary issues. What theuser 100 has access may be limited to a small set of training samples140, which may be obtained by the user 100 through self-development(e.g., for image classification, the user 100 may label images fortraining) or other suitable means. This small set of training samples140 is insufficient to train the student model 120 to achieve areasonable accuracy. Therefore, a training sample generator 150 may betrained to produce more (synthetic) training samples 160 to perform theknowledge distillation between the teacher model 110 and the studentmodel 120.

The goal of the training sample generator 150 is to generate newtraining samples 160 with similar data distribution as the small set oftraining samples 140 with regarding to specific features relevant to theintended use of the student model 120. For instance, if the studentmodel 120 is trained for gender classification, the small set oftraining samples 140 may include images of men and women thatencompasses many different features (e.g., size, color, shape) of eyes,ears, noses, mouths, hairs, etc. Among these features, some are morerelevant to gender, such as the hair length, whereas other features areless relevant, such as the color of eyes. The goal of the trainingsample generator 150 is to generate new training samples with similardata distribution as the real training samples (e.g., the small set oftraining samples 140 or the large volume of training samples 130) withregarding to the features relevant to gender classification, such as thehair length (and corresponding labels). The generated new trainingsamples may have different data distribution as the real trainingsamples with regarding to the features irrelevant to genderclassification, such as the color of nose.

FIG. 2 illustrates an exemplary diagram for training a generator 230with few-shot learning in accordance with various embodiments. Thegenerator 230 may be trained to generate synthetic samples forperforming knowledge distillation between a teacher model and a studentmodel.

As shown, when the large volume of real training samples 210 collectedfrom real-world domains are not available for knowledge distillation, asmall set of training samples and the teacher model may be collectivelytreated as an input 220 to train the generator 230. Here, the teachermodel may be a pre-trained neural network for image classification or apre-trained transformer for natural language processing obtained fromanother entity or online. The small set of training samples may refer tolabeled training samples collected from real-word domains. This smallset of training samples may or may not be a part of the large volumereal training samples 210.

FIG. 3 illustrates an exemplary learning criteria 310 for training agenerator with few-shot learning in accordance with various embodiments.Here, the “learning criteria” may be also referred to as a loss functionor an objective function used to train the generator. To further clarifythe novelty of the new learning criteria 310, FIG. 3 illustrates acomparison between a typical (existing) generator learning criteria 300and the new learning criteria 310 for few-shot learning-based generatortraining.

In existing solutions, the generator may be trained based on a largevolume of real data P_(R) 330. The training of the generator may involvetuning the parameters of the generator so that the generated data P_(g)320 have a similar data distribution as P_(R) 330. For example, if thereal data P_(R) 330 contains a million of data samples, during atraining cycle, a large number of generated (synthetic) samples P_(g)320 may be obtained by using the generator. The learning criteria 300 orthe loss function indicates that data distribution distance D_(T)between P_(g) 320 and P_(R) 330 should be minimized, e.g., smaller thana threshold. If this objective is not met yet, the parameters of thegenerator may be tuned to further reduce the data distribution distanceD_(T). The data distribution distance D_(T) may be determined based onsimilarity matches. For example, for each data sample in P_(g) 320,searching the real data P_(R) 330 to identify a match (e.g., thedistance between feature representations of the two samples is below acertain threshold). Depending on the number of matches found, the datadistribution distance D_(T) may be determined.

However, data distribution may accurately describe a data set only whenthe data set has a large number of data samples. If the data set issmall, data distribution may not properly represent the data set. Asdescribed above, the real data P_(R) 330 in many practical applicationshas only a few data samples.

As shown in FIG. 3 , instead of relying on data distribution, thefew-shot learning criteria 310 relies on statistics μ_(T) of the realdata P_(R) 330 and the generated data P_(g) 320. In some embodiments,the statistics matrix μ_(T) of a data set (either the real data P_(R)330 or the generated data P_(g) 320) may include the statistics ofoutputs generated by the teacher model when the data set is passingthrough the teacher model. The outputs may include each tensor generatedat each layer of the teacher model. In some embodiments, the statisticsmatrix μ_(T) may include channel-level statistics of the tensors outputfrom each layer of the teacher model. The channel-level statistics mayinclude a channel mean, a channel variance, a channel k-th order momentwhere k is greater than two, another suitable check-level statistics, orany combination thereof. In some embodiments, the statistics matrixμ_(T) may include a joint-covariance of all channels in each of thetensors output at each layer of the teacher model. In FIG. 3 , “k”refers to the k layers in the teacher model. Therefore, the few-shotlearning criteria 310 may include a sum of distances between thestatistics of tensors generated by each layer of the teacher model whenthe real data P_(R) 330 and the generated data P_(g) 320 are passingthrough the teacher model.

In some embodiments, the few-shot learning criteria 310 may be used as aloss function or an objective function for tuning parameters of thegenerator towards a direction to minimize this sum of the distances.

FIG. 4 illustrates an exemplary flow for training a generator withfew-shot learning in accordance with various embodiments. The generatormay be implemented as a neural network, a deep neural network, aGenerative Adversarial network, or another suitable machine learningmodel. The generator may be trained to explore the latent space in agiven set of real samples P_(R) (also called target samples), andgenerate synthetic samples P_(g) that resemble P_(R). Here, the“resemblance” may refer to the data distribution resemblance betweenP_(g) and P_(R) with regarding to features relevant to the specific useof the training samples. For instance, if the training samples will beused for gender classification, only the data distribution of thefeatures related to gender classification needs to be resembled (e.g., asimilarity score is below a threshold) in P_(g) and P_(R).

Referring to FIG. 4 , a comparison between a typical generator trainingand few-shot learning-based generator training is illustrated. Typicalgenerator training approaches rely on the distance between the realtraining samples and the synthetic samples generated by the generator,and tune the parameters of the generator to minimize the distance. Forexample, a to-be-trained generator may first generate a plurality ofgenerated synthetic samples P_(g). The distance of data distribution inP_(g) and the set of real/target training samples P_(R) may bedetermined as D_(T). This distance D_(T) may be used as a loss to tunethe parameters of the to-be-trained generator. In thesedata-distribution based approaches, the amount of data samples in P_(g)and P_(R) are needed to be large in order to use data distributions torepresent the features of data sets.

While the above-described typical generator training approaches may beapplied to a wide range of use cases in which the real training samplesare sufficient, the few-shot learning-based generator training approachmay be used for more specific use cases involving a pre-trained teachermodel, such as knowledge distillation from the pre-trained teacher modelto a smaller student model, pruning the pre-trained teacher model, ormodel compression of the pre-trained teacher model. In these use cases,the amount of real training samples is usually limited and thus thetypical generator training approaches may not be applicable (e.g., datadistribution is inaccurate for small data sets).

In some embodiments, the few-shot learning-based generator training mayrely on (1) the real training samples, (2) the synthetic trainingsamples generated by the generator, and (3) the teacher model. Thegenerator may first generate a plurality of synthetic training samplesP_(g). Then the P_(g) is fed into the teacher model to obtain aplurality of first statistics μ_(T) ₁ . . . μ_(T) _(k) , where k refersto the number of layers in the teacher model. Similarly, P_(R) is alsofed into the teacher model to obtain a plurality of second statisticsμ′_(T) ₁ . . . μ′_(T) _(k) . The generator is then be trained based on adistance between the plurality of first statistics and the plurality ofsecond statistics.

In some embodiments, the first statistics and the second statistics maybe collected in the same way, except that they are collected in responseto the input data being the synthetic/generated training samples P_(g)and the real/target training samples P_(R), respectively. For example,to the first statistics μ_(T) ₁ . . . μ_(T) _(k) , P_(g) may be fed intothe teacher model one by one or in small batches. Each layer of theteacher model may perform operations like feature extraction, encoding,decoding, and other suitable operations. Thus each layer may generateoutput such as one or more tensors to be consumed by the next layer. Insome embodiments, the first statistics μ_(T) ₁ . . . μ_(T) _(k) may berespectively collected based on the output generated by the k layers inthe teacher model. For example, μ_(T) ₁ may be collected based on theone or more tensors generated by the first layer of the teacher model inresponse to input data from P_(g). In some embodiments, μ_(T) ₁ includechannel-level statistics of the one or more tensors, such as a channelmean, a channel variance, and a channel i-th order moment (i is greaterthan two) of each tensor. For instance, each tensor has a plurality ofchannels, a channel mean may refer to the mean value of all the valueswithin a corresponding channel. In some embodiments, μ_(T) ₁ may includea joint-covariance of all channels in each of the one or more tensorsgenerated by the first layer of the teacher model.

After obtaining the first statistics μ_(T) ₁ . . . μ_(T) _(k) and thesecond statistics μ′_(T) ₁ . . . μ′_(T) _(k) , a distance between thesetwo statistic matrix may be determined as the loss. The parameters ofthe generator may then be tuned to minimize this loss.

In some embodiments, the trained generator may generate a large numberof synthetic samples for knowledge distillation from the teacher modelto a smaller student model. For example, the student model may beinitialized or constructed in a similar structure as the teacher modelbut with fewer parameters. The student model may be trained to resemblethe teacher model in response to the synthetic samples generated by thetrained generator. For example, the synthetic samples may be fed intothe teacher model and the student model to obtain respective layer-leveloutputs of the teacher model and the student model. The distance betweenthe respective layer-level outputs of the teacher model and the studentmodel may be used as a loss to train the student model to minimize thedistance. In some embodiments, the layer-level outputs include featurevectors generated by embedding layers of the teacher model and embeddinglayers of the student model.

FIG. 5 illustrates an exemplary method 500 for training a generator withfew-shot learning in accordance with various embodiments. The method 500may be performed by a device, apparatus, or system for optimizingresource allocation. The operations of the method 500 presented beloware intended to be illustrative. Depending on the implementation, themethod 500 may include additional, fewer, or alternative steps performedin various orders or in parallel.

Block 510 includes obtaining a teacher model and a plurality of trainingsamples. In some embodiments, the teacher model is a pre-trained neuralnetwork for image classification, and the plurality of training samplesare labeled images. In some embodiments, the teacher model is apre-trained transformer for natural language processing.

Block 520 includes generating a plurality of samples using a generator.

Block 530 includes feeding the plurality of generated samples into theteacher model to obtain a plurality of first statistics. In someembodiments, the feeding the plurality of generated samples into theteacher model to obtain the plurality of first statistics comprises:feeding the plurality of generated samples into the teacher model; andobtaining the plurality of first statistics based on outputs of aplurality of layers in the teacher model when the plurality of generatedsamples are passing through the teacher model. In some embodiments, theoutputs comprise one or more tensors generated by each layer of theteacher model, and the determining the plurality of first statisticsbased on the one or more tensors from each layer of the teacher modelcomprises; for each of the one or more tensors, determining one or morechannel-level statistics; and aggregating the one or more channel-levelstatistics from all layers of the teacher model to obtain the pluralityof first statistics. In some embodiments, the one or more channel-levelstatistics comprise one or more of: a channel mean, a channel variance,and a channel k-th order moment where k is greater than two. In someembodiments, the outputs comprise one or more tensors generated by eachlayer of the teacher model, and the plurality of first statisticscomprise a joint-covariance of all channels in each of the one or moretensors.

Block 540 includes feeding the plurality of training samples into theteacher model to obtain a plurality second statistics. In someembodiments, the feeding the plurality of training samples into theteacher model to obtain the plurality second statistics comprises:feeding the plurality of training samples into the teacher model;obtaining the plurality of second statistics based on outputs of aplurality of layers in the teacher model when the plurality of trainingsamples are passing through the teacher model.

Block 550 includes training the generator to minimize a distance betweenthe plurality of first statistics and the plurality of secondstatistics.

In some embodiments, the method 500 may further include performingknowledge distillation from the teacher model to a student model usingthe trained generator. In some embodiments, the performing knowledgedistillation from the teacher model to the student model using thetrained generator comprises: generating a plurality of new trainingsamples by using the trained generator; feeding the plurality of newtraining samples into the teacher model and the student model to obtainrespective layer-level outputs of the teacher model and the studentmodel; determining a distance between the layer-level outputs of theteacher model and the student model; and training the student model tominimize the distance. In some embodiments, the layer-level outputscomprise feature vectors generated by embedding layers of the teachermodel and embedding layers of the student model.

In some embodiments, constructing the student model with a smallernumber of parameters than the teacher model. In some embodiments, thestudent model is trained to perform classification based on one or morefeatures of an input, and a data distribution of the plurality of newtraining samples with regarding to the one or more features is within athreshold distance from a data distribution of the plurality of trainingsamples with regarding to the one or more features.

FIG. 6A illustrates an exemplary method for class-based generatortraining in accordance with various embodiments. There is a potentialissue with the few-shot learning-based generator training (e.g., method500) when the given set of real training samples are collected fromdiverse domains. As described above, the few-shot learning-basedgenerator training relies on the statistics of the tensors generated byeach layer of a teacher model, and the statistics include channel-levelmean, channel-level variance, or other orders of moments (mean is afirst-order moment, variance is a second-order moment). When the realtraining samples are from different domains, two intermediate tensors(e.g., tensors generated by the teacher model in response to two inputsamples from two domains) may have similar channel-level means (orvariances), but it does not necessarily indicate that distribution ofthe values in these channels are similar as the relevant features of theinput samples may be different. To address this issue, it is desirableto separate the real training samples into different groups, and train agenerator for each group. Ideally, the training samples from a sameclass may be grouped together.

For example, for object classification use cases, the training samplesmay include different birds (class 1, denoted as C₁ in FIG. 6A),different cars (class 2, denoted as C₂ in FIG. 6A), and differentbuildings (class 3, denoted as C₃ in FIG. 6A). In some embodiments, theclasses are known to the entity training the generators, and the realtraining samples are properly labeled to show classes. In these cases,the real training samples may be easily grouped based on class labels.For the groups C₁, C₂, and C₃, the training flow described in FIG. 4 andthe method 500 in FIG. 5 may be applied to train correspondinggenerators, denoted as G₁, G₂, and G₃, respectively.

FIG. 6B illustrates an exemplary method for pivot-sample-based generatortraining in accordance with various embodiments. For many practicalmachine learning tasks, the real training samples 610 cannot be groupedbased on the classes because either the class labels are not available,or the concept of “class” does not even exist in the particular usecase. For instance, the teacher model and the to-be-trained smallstudent model are designed for regression (e.g., the model is trainedfor predicting a particular numerical value based on a set of priordata), detection, clustering (e.g., classes are unknown beforehand). Inthese cases, the class-based sample grouping described in FIG. 6A is notapplicable.

As shown in FIG. 6B, one or more pivot samples may be randomly selectedfrom the real training samples 610. Then the real training samples 610may go through classification or clustering operations based on thepivot samples. The pivot samples may be treated as representatives ofmultiple groups. It is possible that two pivot samples are very similar,in which case they may represent the same group. Here, the “similarity”may be quantified based on a distance between vector representations ofthe two pivot samples.

As shown in FIG. 6B, the number of pivot samples directly affects thenumber of groups to be determined. A greater number of pivot samplesmeans a greater number of groups, as well as a smaller number of realtraining samples in each group. If one group has a smaller number ofreal training samples, the training of the generator may suffer lowaccuracy. On the other hand, a smaller number of pivot samples means asmaller number of groups, with each group including a greater number ofreal training samples. However, it may cause one group having realtraining samples from different domains, which may also negativelyaffect the accuracy of the trained generator for that group. Thedetermination of the number of pivot samples is a tradeoff evaluationbetween the number of groups and the number of real training samples ineach group.

In some embodiments, an optimal set of pivot samples may be determinedby an iterative process. For example, a first number of pivot samplesmay be randomly selected from the real training samples 610, and adistance between vector representations of every pair of the selectedpivot samples may be obtained. Based on the distances, a first set ofunique pivot samples may be identified from the first number of pivotsamples. Here, the “unique pivot samples” may exclude the pivot samplesthat have distances smaller than a threshold from another pivot sample.Next, a second number of pivot samples may be randomly selected from thereal training samples 610, and the above-described process may beexecuted against the first set of pivot samples to identify newly foundunique pivot samples. The newly found unique pivot samples may be mergedinto the first set of unique pivot samples to form a new set of uniquepivot samples. If the number of newly found unique pivot samples isbelow a threshold or no new unique pivot samples is found, the iterativeprocess may stop. The new set of unique pivot samples may be the optimalset of pivot samples.

FIG. 6C illustrates an exemplary method training generators withfew-shot learning and pivot samples in accordance with variousembodiments. After an optimal set of pivot samples 620 are selected anda pool of real training data are clustered into groups 630 correspondingto the pivot samples 620, the next step is to train the generators 650for the groups 630. The method for training each generator 650 may adoptthe training flow described in FIG. 4 and the method 500 in FIG. 5 , inwhich a pre-trained teacher model 640 is required. In some embodiments,the goal of the generators 650 is to generate synthetic training samplesfor knowledge distillation from the pre-trained teacher model 640 into aplurality of smaller student models.

For example, for a given group 630 of real training samples, acorresponding generator 650 may be initialized. The generator 650 maygenerate a plurality of synthetic samples. Both the real trainingsamples in the given group 630 and the generated synthetic samples maybe fed into the teacher model 640 to obtain two sets of statisticmatrixes. The distance between the two sets of statistic matrixes may beused as a loss to tune the parameters of the generator 650 to minimizethe distance. In some embodiments, the statistic matrixes may includechannel-level moments (e.g., mean, variance) of each tensor generated byeach layer of the teacher model. After the generators 650 are trained,they may be used to generate a large number of synthetic samples toperform knowledge distillation from the teacher model 640 to studentmodels. The student models respectively correspond to the generators650.

FIG. 7 illustrates an example method 700 for training generators withpivot samples in accordance with various embodiments. The method 700 maybe performed by a device, apparatus, or system for optimizing resourceallocation. The operations of the method 700 presented below areintended to be illustrative. Depending on the implementation, the method700 may include additional, fewer, or alternative steps performed invarious orders or in parallel.

Block 710 includes obtaining a plurality of training samples.

Block 720 includes randomly selecting a set of pivot samples from theplurality of training samples.

Block 730 includes, based on the set of pivot samples, classifying theplurality of training samples to generate a set of groups of trainingsamples respectively corresponding to the set of pivot samples. In someembodiments, the classifying the plurality of training samples togenerate a set of groups of training samples respectively correspondingto the set of pivot samples comprises: generating pivot vectorrepresentations for the set of pivot samples; for each of the pluralityof training samples, generating a vector representation; determiningdistances between the vector representation and each of the pivot vectorrepresentations; identifying one of the set of pivot samplescorresponding to a pivot vector representation having a shortestdistance to the vector representation; and grouping the training samplewith the one pivot sample.

Block 740 includes training a generator for each of the set of groups oftraining samples for generating new samples, wherein a data distributionof the new samples is within a threshold distance from a datadistribution of the group of training samples.

In some embodiments, the method 700 may further include receiving apre-trained teacher model, wherein the training a generator for each ofthe set of groups of training samples for generating new samplescomprises: initializing the generator; generating a plurality of samplesusing the generator; feeding the plurality of generated samples into theteacher model to obtain a plurality of first statistics; feeding theplurality of training samples into the teacher model to obtain aplurality second statistics; and training the generator to minimize adistance between the plurality of first statistics and the plurality ofsecond statistics. In some embodiments, the feeding the plurality ofgenerated samples into the teacher model to obtain the plurality offirst statistics comprises: feeding the plurality of generated samplesinto the teacher model; and obtaining the plurality of first statisticsbased on outputs of a plurality of layers in the teacher model when theplurality of generated samples are passing through the teacher model.

In some embodiments, the outputs comprise one or more tensors generatedby each layer of the teacher model, and the determining the plurality offirst statistics based on the one or more tensors from each layer of theteacher model comprises; for each of the one or more tensors,determining one or more channel-level statistics; and aggregating theone or more channel level statistics from all layers of the teachermodel to obtain the plurality of first statistics. In some embodiments,the one or more channel-level statistics comprise one or more of: achannel mean, a channel variance, and a channel k-th order moment wherek is greater than two. In some embodiments, the outputs comprise one ormore tensors generated by each layer of the teacher model, and theplurality of statistics comprise a joint-covariance of all channels ineach of the one or more tensors.

In some embodiments, the method 700 may further include: constructing astudent model with a smaller number of parameters than the teachermodel; and performing knowledge distillation from the teacher model to astudent model using the trained generators corresponding to the groupsof training samples. In some embodiments, the performing knowledgedistillation from the teacher model to the student model using thetrained generators comprises: generating a plurality of new trainingsamples by using each of the trained generators; feeding the pluralityof new training samples into the teacher model and the student model toobtain respective layer-level outputs of the teacher model and thestudent model; determining a distance between the layer-level outputs ofthe teacher model and the student model; and training the student modelto minimize the distance. In some embodiments, the layer-level outputscomprise feature vectors generated by embedding layers of the teachermodel and embedding layers of the student model. In some embodiments,the teacher model is a pre-trained neural network for imageclassification, and the plurality of training samples are labeledimages. In some embodiments, the teacher model is a pre-trainedtransformer for natural language processing.

FIG. 8 illustrates an example computing device in which any of theembodiments described herein may be implemented. The computing devicemay be used to implement one or more components of the systems and themethods shown in FIGS. 1-7 . The computing device 800 may comprise a bus802 or other communication mechanisms for communicating information andone or more hardware processors 804 coupled with bus 802 for processinginformation. Hardware processor(s) 804 may be, for example, one or moregeneral-purpose microprocessors.

The computing device 800 may also include a main memory 807, such asrandom-access memory (RAM), cache and/or other dynamic storage devices,coupled to bus 802 for storing information and instructions to beexecuted by processor(s) 804. Main memory 807 also may be used forstoring temporary variables or other intermediate information duringexecution of instructions to be executed by processor(s) 804. Suchinstructions, when stored in storage media accessible to processor(s)804, may render computing device 800 into a special-purpose machine thatis customized to perform the operations specified in the instructions.Main memory 807 may include non-volatile media and/or volatile media.Non-volatile media may include, for example, optical or magnetic disks.Volatile media may include dynamic memory. Common forms of media mayinclude, for example, a floppy disk, a flexible disk, hard disk,solid-state drive, magnetic tape, or any other magnetic data storagemedium, a CD-ROM, any other optical data storage medium, any physicalmedium with patterns of holes, a RAM, a DRAM, a PROM, and EPROM, aFLASH-EPROM, NVRAM, any other memory chip or cartridge, or networkedversions of the same.

The computing device 800 may implement the techniques described hereinusing customized hard-wired logic, one or more ASICs or FPGAs, firmwareand/or program logic which in combination with the computing device maycause or program computing device 800 to be a special-purpose machine.According to one embodiment, the techniques herein are performed bycomputing device 800 in response to processor(s) 804 executing one ormore sequences of one or more instructions contained in main memory 807.Such instructions may be read into main memory 807 from another storagemedium, such as storage device 809. Execution of the sequences ofinstructions contained in main memory 807 may cause processor(s) 804 toperform the process steps described herein. For example, theprocesses/methods disclosed herein may be implemented by computerprogram instructions stored in main memory 807. When these instructionsare executed by processor(s) 804, they may perform the steps as shown incorresponding figures and described above. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The computing device 800 also includes a communication interface 810coupled to bus 802. Communication interface 810 may provide a two-waydata communication coupling to one or more network links that areconnected to one or more networks. As another example, communicationinterface 810 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN (or WAN component tocommunicated with a WAN). Wireless links may also be implemented.

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented engines may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented engines may be distributed across a number ofgeographic locations.

Each of the processes, methods, and algorithms described in thepreceding sections may be embodied in, and fully or partially automatedby, code modules executed by one or more computer systems or computerprocessors comprising computer hardware. The processes and algorithmsmay be implemented partially or wholly in application-specificcircuitry.

When the functions disclosed herein are implemented in the form ofsoftware functional units and sold or used as independent products, theycan be stored in a processor executable non-volatile computer-readablestorage medium. Particular technical solutions disclosed herein (inwhole or in part) or aspects that contributes to current technologiesmay be embodied in the form of a software product. The software productmay be stored in a storage medium, comprising a number of instructionsto cause a computing device (which may be a personal computer, a server,a network device, and the like) to execute all or some steps of themethods of the embodiments of the present application. The storagemedium may comprise a flash drive, a portable hard drive, ROM, RAM, amagnetic disk, an optical disc, another medium operable to store programcode, or any combination thereof.

Particular embodiments further provide a system comprising a processorand a non-transitory computer-readable storage medium storinginstructions executable by the processor to cause the system to performoperations corresponding to steps in any method of the embodimentsdisclosed above. Particular embodiments further provide a non-transitorycomputer-readable storage medium configured with instructions executableby one or more processors to cause the one or more processors to performoperations corresponding to steps in any method of the embodimentsdisclosed above.

Embodiments disclosed herein may be implemented through a cloudplatform, a server or a server group (hereinafter collectively the“service system”) that interacts with a client. The client may be aterminal device, or a client registered by a user at a platform, whereinthe terminal device may be a mobile terminal, a personal computer (PC),and any device that may be installed with a platform applicationprogram.

The various features and processes described above may be usedindependently of one another or may be combined in various ways. Allpossible combinations and sub-combinations are intended to fall withinthe scope of this disclosure. In addition, certain method or processblocks may be omitted in some implementations. The methods and processesdescribed herein are also not limited to any particular sequence, andthe blocks or states relating thereto can be performed in othersequences that are appropriate. For example, described blocks or statesmay be performed in an order other than that specifically disclosed, ormultiple blocks or states may be combined in a single block or state.The example blocks or states may be performed in serial, in parallel, orin some other manner. Blocks or states may be added to or removed fromthe disclosed example embodiments. The exemplary systems and componentsdescribed herein may be configured differently than described. Forexample, elements may be added to, removed from, or rearranged comparedto the disclosed example embodiments.

The various operations of exemplary methods described herein may beperformed, at least partially, by an algorithm. The algorithm may becomprised in program codes or instructions stored in a memory (e.g., anon-transitory computer-readable storage medium described above). Suchalgorithm may comprise a machine learning algorithm. In someembodiments, a machine learning algorithm may not explicitly programcomputers to perform a function but can learn from training samples tomake a prediction model that performs the function.

The various operations of exemplary methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented enginesthat operate to perform one or more operations or functions describedherein.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented engines. Moreover, the one or more processors mayalso operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an Application ProgramInterface (API)).

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented engines may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented engines may be distributed across a number ofgeographic locations.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

As used herein, “or” is inclusive and not exclusive, unless expresslyindicated otherwise or indicated otherwise by context. Therefore,herein, “A, B, or C” means “A, B, A and B, A and C, B and C, or A, B,and C,” unless expressly indicated otherwise or indicated otherwise bycontext. Moreover, “and” is both joint and several, unless expresslyindicated otherwise or indicated otherwise by context. Therefore,herein, “A and B” means “A and B, jointly or severally,” unlessexpressly indicated otherwise or indicated otherwise by context.Moreover, plural instances may be provided for resources, operations, orstructures described herein as a single instance. Additionally,boundaries between various resources, operations, engines, and datastores are somewhat arbitrary, and particular operations are illustratedin a context of specific illustrative configurations. Other allocationsof functionality are envisioned and may fall within a scope of variousembodiments of the present disclosure. In general, structures andfunctionality presented as separate resources in the exampleconfigurations may be implemented as a combined structure or resource.Similarly, structures and functionality presented as a single resourcemay be implemented as separate resources. These and other variations,modifications, additions, and improvements fall within a scope ofembodiments of the present disclosure as represented by the appendedclaims. The specification and drawings are, accordingly, to be regardedin an illustrative rather than a restrictive sense.

The term “include” or “comprise” is used to indicate the existence ofthe subsequently declared features, but it does not exclude the additionof other features. Conditional language, such as, among others, “can,”“could,” “might,” or “may,” unless specifically stated otherwise, orotherwise understood within the context as used, is generally intendedto convey that certain embodiments include, while other embodiments donot include, certain features, elements and/or steps. Thus, suchconditional language is not generally intended to imply that features,elements and/or steps are in any way required for one or moreembodiments or that one or more embodiments necessarily include logicfor deciding, with or without user input or prompting, whether thesefeatures, elements and/or steps are included or are to be performed inany particular embodiment.

Although an overview of the subject matter has been described withreference to specific example embodiments, various modifications andchanges may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure. Such embodimentsof the subject matter may be referred to herein, individually orcollectively, by the term “invention” merely for convenience and withoutintending to voluntarily limit the scope of this application to anysingle disclosure or concept if more than one is, in fact, disclosed.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

The invention claimed is:
 1. A computer-implemented method, comprising:obtaining a plurality of training samples without labels forclassification; selecting a plurality of pivot samples from theplurality of training samples, wherein the selecting comprises aniterative process comprising: executing a first iteration comprising:randomly selecting multiple first pivot samples from the plurality oftraining samples; determining a vectorial distance between every pair inthe multiple first pivot samples to obtain a plurality of vectorialdistances; and identifying and selecting a first set of pivot samplesbased on the plurality of vectorial distances, wherein the first set ofpivot samples excludes one of the first pivot samples having a vectorialdistance smaller than a threshold value from another of the first pivotsample; executing a second iteration by repeating the first iteration toobtain a second set of pivot samples; merging the first set of pivotsamples and the second set of pivot samples to obtain the plurality ofpivot samples; based on the plurality of pivot samples, grouping theplurality of training samples to generate a plurality of groups oftraining samples respectively corresponding to the plurality of pivotsamples; and training a generator for each of the plurality of groups oftraining samples for generating new samples using few-shot learning,wherein a data distribution of the new samples is within a thresholddistance from a data distribution of the group of training samples,wherein the training comprises: receiving a teacher model trained toperform tasks on the plurality of training samples; generating aplurality of samples using the generator; feeding the plurality ofgenerated samples into the teacher model to obtain a plurality of firststatistics, wherein the plurality of first statistics comprise one ormore channel-level statistics comprising: a channel mean, a channelvariance, and a channel k-th order moment of all values within acorresponding channel in a tensor generated at a layer of the teachermodel, wherein k is greater than two; feeding the plurality of trainingsamples into the teacher model to obtain a plurality second statistics;and training the generator to minimize a distance between the pluralityof first statistics and the plurality of second statistics.
 2. Themethod of claim 1, wherein the feeding the plurality of generatedsamples into the teacher model to obtain the plurality of firststatistics comprises: feeding the plurality of generated samples intothe teacher model; and obtaining the plurality of first statistics basedon outputs of a plurality of layers in the teacher model when theplurality of generated samples are passing through the teacher model. 3.The method of claim 2, wherein the outputs comprise one or more tensorsgenerated by a plurality of layers of the teacher model, and thedetermining of the plurality of first statistics based on the one ormore tensors from the plurality of layers of the teacher modelcomprises: for each of the one or more tensors, determining the one ormore channel-level statistics; and aggregating the one or more channellevel statistics to obtain the plurality of first statistics.
 4. Themethod of claim 3, wherein the outputs comprise one or more tensorsgenerated by a plurality of layers of the teacher model, and theplurality of statistics comprise a joint-covariance of all channels ineach of the one or more tensors.
 5. The method of claim 1, furthercomprising: constructing a student model with a smaller number ofparameters than the teacher model; and performing knowledge distillationfrom the teacher model to a student model using the trained generatorscorresponding to the groups of training samples.
 6. The method of claim5, wherein the performing knowledge distillation from the teacher modelto the student model using the trained generators comprises: generatinga plurality of new training samples by using each of the trainedgenerators; feeding the plurality of new training samples into theteacher model and the student model to obtain respective layer-leveloutputs of the teacher model and the student model; determining adistance between the layer-level outputs of the teacher model and thestudent model; and training the student model to minimize the distance.7. The method of claim 6, wherein the layer-level outputs comprisefeature vectors generated by embedding layers of the teacher model andembedding layers of the student model.
 8. The method of claim 1, whereinthe teacher model is a pre-trained neural network for imageclassification.
 9. The method of claim 1, wherein the teacher model is apre-trained transformer for natural language processing.
 10. A systemcomprising one or more processors and one or more non-transitorycomputer-readable memories coupled to the one or more processors andconfigured with instructions executable by the one or more processors tocause the system to perform operations comprising: obtaining a pluralityof training samples without labels for classification; selecting aplurality of pivot samples from the plurality of training samples,wherein the selecting comprises an iterative process comprising:executing a first iteration comprising: randomly selecting multiplefirst pivot samples from the plurality of training samples; determininga vectorial distance between every pair in the multiple first pivotsamples to obtain a plurality of vectorial distances; and identifyingand selecting a first set of pivot samples based on the plurality ofvectorial distances, wherein the first set of pivot samples excludes oneof the first pivot samples having a vectorial distance smaller than athreshold value from another of the first pivot sample; executing asecond iteration by repeating the first iteration to obtain a second setof pivot samples; merging the first set of pivot samples and the secondset of pivot samples to obtain the plurality of pivot samples; based onthe plurality of pivot samples, grouping the plurality of trainingsamples to generate a plurality of groups of training samplesrespectively corresponding to the plurality of pivot samples; andtraining a generator for each of the plurality of groups of trainingsamples for generating new samples using few-shot learning, wherein adata distribution of the new samples is within a threshold distance froma data distribution of the group of training samples, wherein thetraining comprises: receiving a teacher model trained to perform taskson the plurality of training samples; generating a plurality of samplesusing the generator; feeding the plurality of generated samples into theteacher model to obtain a plurality of first statistics, wherein theplurality of first statistics comprise one or more channel-levelstatistics comprising: a channel mean, a channel variance, and a channelk-th order moment of all values within a corresponding channel in atensor generated at a layer of the teacher model, wherein k is greaterthan two; feeding the plurality of training samples into the teachermodel to obtain a plurality second statistics; and training thegenerator to minimize a distance between the plurality of firststatistics and the plurality of second statistics.
 11. The system ofclaim 10, wherein the feeding the plurality of generated samples intothe teacher model to obtain the plurality of first statistics comprises:feeding the plurality of generated samples into the teacher model; andobtaining the plurality of first statistics based on outputs of aplurality of layers in the teacher model when the plurality of generatedsamples are passing through the teacher model.
 12. A non-transitorycomputer-readable storage medium configured with instructions executableby one or more processors to cause the one or more processors to performoperations comprising: obtaining a plurality of training samples withoutlabels for classification; selecting a plurality of pivot samples fromthe plurality of training samples, wherein the selecting comprises aniterative process comprising: executing a first iteration comprising:randomly selecting multiple first pivot samples from the plurality oftraining samples; determining a vectorial distance between every pair inthe multiple first pivot samples to obtain a plurality of vectorialdistances; and identifying and selecting a first set of pivot samplesbased on the plurality of vectorial distances, wherein the first set ofpivot samples excludes one of the first pivot samples having a vectorialdistance smaller than a threshold value from another of the first pivotsample; executing a second iteration by repeating the first iteration toobtain a second set of pivot samples; merging the first set of pivotsamples and the second set of pivot samples to obtain the plurality ofpivot samples; based on the plurality of pivot samples, grouping theplurality of training samples to generate a plurality of groups oftraining samples respectively corresponding to the plurality of pivotsamples; and training a generator for each of the plurality of groups oftraining samples for generating new samples using few-shot learning,wherein a data distribution of the new samples is within a thresholddistance from a data distribution of the group of training samples,wherein the training comprises: receiving a teacher model trained toperform tasks on the plurality of training samples; generating aplurality of samples using the generator; feeding the plurality ofgenerated samples into the teacher model to obtain a plurality of firststatistics, wherein the plurality of first statistics comprise one ormore channel-level statistics comprising: a channel mean, a channelvariance, and a channel k-th order moment of all values within acorresponding channel in a tensor generated at a layer of the teachermodel, wherein k is greater than two; feeding the plurality of trainingsamples into the teacher model to obtain a plurality second statistics;and training the generator to minimize a distance between the pluralityof first statistics and the plurality of second statistics.
 13. Thestorage medium of claim 12, wherein the feeding the plurality ofgenerated samples into the teacher model to obtain the plurality offirst statistics comprises: feeding the plurality of generated samplesinto the teacher model; and obtaining the plurality of first statisticsbased on outputs of a plurality of layers in the teacher model when theplurality of generated samples are passing through the teacher model.14. The system of claim 10, wherein the plurality of first statisticsare determined based on one or more tensors from a plurality of layersof the teacher model, and the determining comprises: for each of the oneor more tensors, determining the one or more channel-level statistics;and aggregating the one or more channel level statistics to obtain theplurality of first statistics.
 15. The system of claim 10, wherein theoperations further comprise: constructing a student model with a smallernumber of parameters than the teacher model; and performing knowledgedistillation from the teacher model to a student model using the trainedgenerators corresponding to the groups of training samples.
 16. Thesystem of claim 15, wherein the performing knowledge distillation fromthe teacher model to the student model using the trained generatorscomprises: generating a plurality of new training samples by using eachof the trained generators; feeding the plurality of new training samplesinto the teacher model and the student model to obtain respectivelayer-level outputs of the teacher model and the student model;determining a distance between the layer-level outputs of the teachermodel and the student model; and training the student model to minimizethe distance.
 17. The storage medium of claim 12, wherein the operationsfurther comprise: constructing a student model with a smaller number ofparameters than the teacher model; and performing knowledge distillationfrom the teacher model to a student model using the trained generatorscorresponding to the groups of training samples.
 18. The storage mediumof claim 17, wherein the performing knowledge distillation from theteacher model to the student model using the trained generatorscomprises: generating a plurality of new training samples by using eachof the trained generators; feeding the plurality of new training samplesinto the teacher model and the student model to obtain respectivelayer-level outputs of the teacher model and the student model;determining a distance between the layer-level outputs of the teachermodel and the student model; and training the student model to minimizethe distance.